haojin2 commented on a change in pull request #15349: Numpy Tensordot Operator
URL: https://github.com/apache/incubator-mxnet/pull/15349#discussion_r302263201
##########
File path: tests/python/unittest/test_numpy_op.py
##########
@@ -26,7 +26,151 @@
from mxnet.test_utils import check_numeric_gradient
from common import assertRaises, with_seed
import random
+import collections
+@with_seed()
+@npx.use_np_shape
+def test_np_tensordot():
+ class TestTensordot(HybridBlock):
+ def __init__(self, axes):
+ super(TestTensordot, self).__init__()
+ self._axes = axes
+
+ def hybrid_forward(self, F, a, b):
+ return F.np.tensordot(a, b, self._axes)
+
+ def tensordot_backward(a, b, axes = 2):
+ if (a.ndim < 1) or (b.ndim < 1):
+ raise ValueError('An input is zero-dim')
+
+ if isinstance(axes, collections.abc.Sequence):
+ if len(axes) != 2:
+ raise ValueError('Axes must consist of two arrays.')
+ a_axes_summed, b_axes_summed = axes
+ if _np.isscalar(a_axes_summed):
+ a_axes_summed = a_axes_summed,
+ if _np.isscalar(b_axes_summed):
+ b_axes_summed = b_axes_summed,
+ else:
+ a_axes_summed = [i + a.ndim - axes for i in range(axes)]
+ b_axes_summed = [i for i in range(axes)]
+
+ if len(a_axes_summed) != len(b_axes_summed):
+ raise ValueError('Axes length mismatch')
+
+ a_axes_remained = []
+ for i in range(a.ndim):
+ if not (i in a_axes_summed):
+ a_axes_remained.append(i)
+ a_axes = a_axes_remained[:] + a_axes_summed[:]
+
+ b_axes_remained = []
+ for i in range(b.ndim):
+ if not (i in b_axes_summed):
+ b_axes_remained.append(i)
+ b_axes = b_axes_summed[:] + b_axes_remained[:]
+
+ ad1 = _np.prod([a.shape[i] for i in a_axes_remained]) if
len(a_axes_remained) > 0 else 1
+ ad2 = _np.prod([a.shape[i] for i in a_axes_summed]) if
len(a_axes_summed) > 0 else 1
+ bd1 = _np.prod([b.shape[i] for i in b_axes_summed]) if
len(b_axes_summed) > 0 else 1
+ bd2 = _np.prod([b.shape[i] for i in b_axes_remained]) if
len(b_axes_remained) > 0 else 1
+
+ out_grad = _np.ones((ad1, bd2))
+
+ new_a = _np.transpose(a, a_axes)
+ new_a_shape = new_a.shape[:]
+ new_a = new_a.reshape((ad1, ad2))
+ new_b = _np.transpose(b, b_axes)
+ new_b_shape = new_b.shape[:]
+ new_b = new_b.reshape((bd1, bd2))
+
+ reverse_a_axes = [0 for i in a_axes]
+ for i in range(len(a_axes)):
+ reverse_a_axes[a_axes[i]] = i
+
+ reverse_b_axes = [0 for i in b_axes]
+ for i in range(len(b_axes)):
+ reverse_b_axes[b_axes[i]] = i
+
+ grad_b = _np.dot(new_a.T, out_grad).reshape(new_b_shape)
+ grad_b = _np.transpose(grad_b, reverse_b_axes)
+ grad_a = _np.dot(out_grad, new_b.T).reshape(new_a_shape)
+ grad_a = _np.transpose(grad_a, reverse_a_axes)
+
+ return [grad_a, grad_b]
+
+ # test non zero size input
+ tensor_shapes = [
+ ((3, 5), (5, 4), 1), # (a_shape, b_shape, axes)
+ ((3,), (3,), 1),
+ ((3, 4, 5, 6, 7), (5, 6, 7, 1, 2), 3),
+ ((3, 5, 4, 6, 7), (7, 6, 5, 1, 2), [[1, 3, 4], [2, 1, 0]]),
+ ((2, 2), (2, 2), 2),
+ ((3, 5, 4), (5, ), [[1], [0]]),
+ ((2,), (2, 3), 1),
+ ((3,), (3,), 0),
+ ((2,), (2, 3), 0),
+ ((3, 5, 4), (5, ), 0)
+ ]
+
+ for hybridize in [True, False]:
+ for a_shape, b_shape, axes in tensor_shapes:
+ for dtype in [_np.float32, _np.float64]:
+ test_tensordot = TestTensordot(axes)
+ if hybridize:
+ test_tensordot.hybridize()
+ a = rand_ndarray(shape = a_shape, dtype =
dtype).as_np_ndarray()
+ b = rand_ndarray(shape = b_shape, dtype =
dtype).as_np_ndarray()
+ a.attach_grad()
+ b.attach_grad()
+
+ np_out = _np.tensordot(a.asnumpy(), b.asnumpy(), axes)
+ with mx.autograd.record():
+ mx_out = test_tensordot(a, b)
+ assert mx_out.shape == np_out.shape
+ assert_almost_equal(mx_out.asnumpy(), np_out, rtol = 1e-3,
atol = 1e-5)
+ mx_out.backward()
+ np_backward = tensordot_backward(a.asnumpy(), b.asnumpy(),
axes)
+ assert_almost_equal(a.grad.asnumpy(), np_backward[0], rtol =
1e-3, atol=1e-5)
+ assert_almost_equal(b.grad.asnumpy(), np_backward[1], rtol =
1e-3, atol=1e-5)
+
+ # Test imperative once again
+ mx_out = np.tensordot(a, b, axes)
+ np_out = _np.tensordot(a.asnumpy(), b.asnumpy(), axes)
+ assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3,
atol=1e-5)
+
+ # test numeric gradient
+ a_sym = mx.sym.Variable("a").as_np_ndarray()
+ b_sym = mx.sym.Variable("b").as_np_ndarray()
+ mx_sym = mx.sym.np.tensordot(a_sym, b_sym,
axes).as_nd_ndarray()
+ check_numeric_gradient(mx_sym, {"a": a.as_nd_ndarray(), "b":
b.as_nd_ndarray()},
+ rtol=1e-2, atol=1e-2, dtype = dtype)
+
+ # test zero size input
+ zero_shapes = [
+ ((3, 0), (0, 5), 1),
+ ((0, 3), (3, 5), 1)
+ ]
+
+ for hybridize in [True, False]:
+ for a_shape, b_shape, axes in zero_shapes:
+ for dtype in [_np.float32, _np.float64]:
+ test_tensordot = TestTensordot(axes)
+ if hybridize:
+ test_tensordot.hybridize()
+ a = rand_ndarray(shape = a_shape, dtype =
dtype).as_np_ndarray()
+ b = rand_ndarray(shape = b_shape, dtype =
dtype).as_np_ndarray()
+
+ np_out = _np.tensordot(a.asnumpy(), b.asnumpy(), axes)
+ with mx.autograd.record():
+ mx_out = test_tensordot(a, b)
+ assert mx_out.shape == np_out.shape
+ assert_almost_equal(mx_out.asnumpy(), np_out, rtol = 1e-3,
atol = 1e-5)
+
+ # Test imperative once again
+ mx_out = np.tensordot(a, b, axes)
+ np_out = _np.tensordot(a.asnumpy(), b.asnumpy(), axes)
+ assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3,
atol=1e-5)
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